Evandro Gouvea

Binaural and Multiple-Microphone Signal Processing Motivated by Auditory Perception

It is well known that binaural processing is very useful for separating incoming sound sources as well as for improving the intelligibility of speech in reverberant environments. This paper describes and compares a number of ways in which the classic model of interaural cross-correlation proposed by Jeffress, quantified by Colburn, and further elaborated by Blauert, Lindemann, and others, can be applied to improving the accuracy of automatic speech recognition systems operating in cluttered, noisy, and reverberant environments. Typical implementations begin with an abstraction of cross-correlation of the incoming signals after nonlinear monaural bandpass processing, but there are many alternative implementation choices that can be considered. Typical implementations differ in the ways in which an enhanced version of the desired signal is developed using binaural principles, in the extent to which specific processing mechanisms are used to impose suppression motivated by the precedence effect, and in the precise mechanism used to extract interaural time differences.

Polyaural array processing for robust automatic speech recognition in noisy and reverberant environments

It is well known that human binaural processing is very useful for separating incoming sound sources as well as for improving the intelligibility of speech in reverberant environments. In this paper we present a new method of signal processing for robust speech recognition using multiple microphones. The method, loosely based on the human binaural hearing system, consists of passing the speech signals detected by multiple microphones through bandpass filtering and nonlinear halfwave rectification operations, and then cross‐correlating the outputs from each channel within each frequency band. These operations provide rejection of off‐axis interfering signals. These operations are repeated (in a non‐physiological fashion) for the negative of the signal, and an estimate of the desired signal is obtained by combining the positive and negative outputs. We demonstrate that the use of this approach provides substantially better recognition accuracy than delay‐and‐sum beamforming using the same sensors for target...